In our paper, the DTI-GAT are trained end-to-end using gradient descent based on Adam optimizer [1]}. We perform batch gradient descent using the full dataset for every training iteration, which is a viable option as long as datasets fit in memory. Base on the implementation of PyTorch Geometric [2]}, we use a sparse representation for A. The memory requirement is \(\mathcal {O} (|\varepsilon |)\) , which is linear in the number of edges. The learning rate \(\alpha \)  is 0.0005 and the weight decay is \(5e-4\) . Stochasticity in the training process is by dropout [3]}, which is set to 0.3. The model is trained until converging for each fold of the cross-validation,
for which we set the epoch number to be 4000, 8000, 10000, 15000 for Nuclear Receptor, GPCR, Ion Channel and Enzyme, respectively.
